Background Endocrine malignancies (including thyroid, adrenal, pancreas, parathyroid, and neuroendocrine cancers) are among the fastest growing cancer diagnoses in the United States, but it is difficult to distinguish benign from malignant tumors by routine clinical, laboratory, and imaging studies. So, even patients who have seemingly benign endocrine tumors often choose to undergo surgery to get a definitive diagnosis in the hopes of ruling out cancer. Most patients with endocrine cancers have a relatively good prognosis. However, anywhere from 10% to 40% (depending on tumor type) have aggressive disease which often cannot be reliably determined at the time of initial treatment. Prognostic markers which can reliably risk stratify patients with high risk of recurrence and death would help determine which patients should receive aggressive initial treatment and close follow up. Furthermore, prognostic markers may also help identify which patients are likely to respond to standard therapy and which patients do not respond to standard therapy if a distinct molecular phenotype is identified. Summary We are using a pan-genomic (mRNA and microRNA expression, and global methylation) profiling approach in human tumor tissue samples to identify candidate diagnostic and prognostic markers for endocrine malignancies (thyroid, adrenal, neuroendocrine pancreas). Several strategies will be used for marker identifications and validation. High dimensional bioinformatics approach will be used to determine if molecular classification is accurate enough for diagnosis and prognosis in well annotated samples with clinical follow up data. The candidate markers will be validated in an independent sample set to test their diagnostic and prognostic accuracy. Furthermore, ex vivo tumor biopsy samples, animal models for serum markers and clinical tumor biopsy samples will be used to validate candidate diagnostic and prognostic markers and determine their clinical utility. The molecular basis for endocrine cancer initiation and progression is poorly understood. Given we will have pan-genomic expression profiling data, we will determine whether microRNA misexpression and or epigenetic (promoter methylation) changes account for the genes found to be deregulated in endocrine cancers. The master microRNA regulators of genes dysregulated in endocrine cancers will then be tested in vitro using functional genomic approaches to confirm their role in gene expression regulation. For genes possibly regulated by promoter methylation based on the global methylation profiling studies, we will use gene specific promoter methylation analysis and demethylating agent treatment in cell lines to decipher the importance of epigenetic gene expression regulation in endocrine cancers. It is hoped such an integrated bioinformatics approach and functional genomics approach in in vitro models of endocrine cancer cells will shed light on the main mechanisms of gene expression deregulation in endocrine cancers.